Researchers at First Moscow State Medical University named after IM. Sechenov, in collaboration with the Ministry of Health of the Russian Federation, are developing a remote system to determine a patient’s blood group. The concept hinges on a healthcare worker uploading a photo of a blood sample mixed with a reagent into a dedicated application. The digital product, which is expected to enter the medical market within the next couple of years, aims to reduce the risk of complications during blood transfusion by speeding up and standardizing the initial typing process. This update was shared with socialbites.ca by university representatives.
The team acknowledges a persistent challenge in the country: a shortage of laboratory diagnostic specialists who must verify blood group results assigned by the attending physician. With the new tool, a clinician can transmit, via a smartphone app, a photo of the prepared blood test and a reagent mix to a program that generates a preliminary assessment of the patient’s blood type. The laboratory diagnostician can then remotely confirm this result by saving the analysis outcome and the image in the medical information system, enabling a streamlined, gap-aware workflow. This is the insight offered by Pavel Tregub, associate professor in the Department of Pathophysiology at Sechenov University and a key author of the PhotoResus software package, as explained to socialbites.ca.
The digital service rests on artificial intelligence technology and will be accessible to healthcare personnel through any smartphone. By applying advanced image analysis algorithms, the system evaluates the presence of agglutination, or micro clots, in blood after it is mixed with a reagent that contains antibodies against red blood cells. In traditional practice, doctors visually assess the adhesion of red blood cells on blood drops. Today’s tech landscape makes it possible to create a digital stand-in for the human eye, enabling rapid, scalable assessments. With early interest from industry partners, the project team plans to introduce the product to the medical market within two years, expanding the reach of accurate typing to more clinics and hospitals.
Annual statistics in Russia show more than 1.5 million blood transfusions and related components, with roughly 30,000 cases accompanied by complications arising from partial antigenic mismatches between donor and recipient, across various blood group systems, including rare variants. The software developers believe many of these adverse events can be prevented through more reliable, computer-assisted screening and verification. By reducing human factor errors and cutting the need for some equipment and consumables, the tool could lower operating costs and improve safety across multiple care settings. The authors see particular value for surgical units, obstetric-gynecological departments, and smaller, remote hospitals where access to on-site specialized labs is limited, enabling timely and accurate donor-recipient matching when seconds matter most.
As the field advances, the project team envisions adoption that complements existing transfusion medicine workflows rather than replacing essential clinical judgment. The technology is designed to support clinicians by providing a robust second check and facilitating remote collaboration among team members who might be geographically separated. This approach aligns with broader health information system goals: to speed up decision-making, improve traceability of samples and results, and ensure consistent documentation within patient records. While still in development, the initiative stands as a noteworthy example of how digital tools can augment traditional laboratory processes without compromising safety or quality, particularly in environments where access to specialized expertise varies widely.
In the broader context of patient safety and transfusion medicine, the move toward digital assessment tools reflects a growing trend in which artificial intelligence augments human expertise. By combining image analysis with secure data capture and remote confirmation, the system aspires to deliver timely guidance while maintaining clinical oversight. The eventual rollout will likely involve rigorous validation studies, regulatory review, and pilot implementations across diverse clinical settings to ensure reliability, user-friendly operation, and compatibility with existing information systems. If successful, this work could pave the way for similar AI-assisted diagnostics in other areas of laboratory medicine, contributing to safer transfusion practices and more efficient use of healthcare resources.